E2E solutions have been a focus of our research group for some time [1]}. Fundamental to this pursuit is operation upon the raw signal. A recent attempt [2]} adopted the RawNet2 architecture [3]}, [4]}. Using a bank of sinc-shaped filters, it operates directly upon the raw audio waveform through time-domain convolution, with the remaining network components being optimised in the usual way. Results show that systems that use automatically learned features are competitive and complementary to systems that use hand crafted features. While these findings are encouraging, improvements to performance are perhaps only modest. Despite the emphasis upon the E2E learning of both features and classifier, one aspect of our model remains hand-crafted [2]}. This is also the case for every E2E solution proposed thus far [6]}, [7]}, [3]}; the network parameters are learned, but the network architecture is still hand-crafted.
